Consistency Regularization is crucial in semi-supervised learning (SSL). It is assumed that a classifier should output the same class probability for an unlabeled sample even after it is augmented. Prior works [1]} apply the consistency regularization loss on different augmentations of an unlabeled sample. After that, several variants [2]}, [3]} are further proposed to extend its applications.
Among them, [2]} also uses MixUp by mixing unlabeled samples and their guessed labels. The main difference between our method and them is that we don't guess the hard labels of unlabeled samples, thereby reducing undesirable noise.
